Show simple item record

Multi-Agent Reinforcement Symbolic Regression for the Fatigue Life Prediction of Aircraft Landing Gear

dc.contributor.authorYi-Pin Sun
dc.contributor.authorHaozhe Feng
dc.contributor.authorBaiyang Zheng
dc.contributor.authorJiong-Ran Wen
dc.contributor.authorAi-Fang Chao
dc.contributor.authorCheng-Wei Fei
dc.contributor.otherDepartment of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
dc.contributor.otherDepartment of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
dc.contributor.otherDepartment of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
dc.contributor.otherDepartment of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
dc.contributor.otherAECC Hunan Power Machinery Research Institute, Zhuzhou 412002, China
dc.contributor.otherDepartment of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
dc.date.accessioned2025-08-27T13:59:50Z
dc.date.accessioned2025-10-08T08:35:39Z
dc.date.available2025-10-08T08:35:39Z
dc.date.issued01-08-2025
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/36163
dc.description.abstractAccurate fatigue life prediction of aircraft landing gear is crucial for ensuring flight safety and preventing catastrophic structural failures. However, traditional empirical methods face significant limitations in capturing complex multiaxial loading conditions, while machine learning approaches suffer from lack of interpretability in critical safety applications. To address the dual challenges of prediction accuracy and model interpretability, a multi-agent reinforced symbolic regression (MA-RSR) framework is proposed by integrating multi-agent reinforcement learning with symbolic regression (SR) techniques. Specifically, MA-RSR employs a collaborative mechanism that decomposes complex mathematical expressions into parallel components constructed by independent agents, effectively addressing the search space explosion problem in traditional SR. The system incorporates Transformer-based architecture to enhance symbolic selection capabilities, while an intelligent masking mechanism ensures mathematical rationality through multi-level constraints. To demonstrate effectiveness of the proposed method, validation is conducted using SAE4340 steel multiaxial fatigue data and landing gear finite element simulation. The MA-RSR framework successfully discovers two mathematical expressions achieving R<sup>2</sup> of 0.96. Compared to traditional empirical formulas, MA-RSR achieves prediction accuracy improvements exceeding 50% while providing complete interpretability that machine learning methods lack. Furthermore, the multi-agent collaborative mechanism significantly enhances search efficiency through parallel expression construction compared to existing symbolic regression approaches.
dc.language.isoEN
dc.publisherMDPI AG
dc.subject.lccMotor vehicles. Aeronautics. Astronautics
dc.titleMulti-Agent Reinforcement Symbolic Regression for the Fatigue Life Prediction of Aircraft Landing Gear
dc.typeArticle
dc.description.keywordsfatigue life prediction
dc.description.keywordsmulti-agent reinforcement learning
dc.description.keywordssymbolic regression
dc.description.keywordsaircraft landing gear
dc.description.keywordsmultiaxial fatigue
dc.description.doi10.3390/aerospace12080718
dc.title.journalAerospace
dc.identifier.e-issn2226-4310
dc.identifier.oaioai:doaj.org/journal:9224e35724634bb49a9092cdbf1deeda
dc.journal.infoVolume 12, Issue 8


This item appears in the following Collection(s)

Show simple item record